Evaluation of New Diagnostic Methods of Cardio-embolic Related (Atrial Fibrillation) Cerebral Infarction...
StrokeThe aim of the study is to estimate the contribution of abdominal imaging by magnetic resonance Imaging (MRI) and abdominal scanner in the detection of subdiaphragmatic infarction associated to the atrial fibrillation in the cerebral infarction.
Monitoring Strategies for the Detection of Atrial Fibrillation in Patients With Cryptogenic Stroke...
StrokeAtrial FibrillationRandomized clinical trial comparing two monitoring strategies, the use of a 48-hour Holter (routine care branch) and an event recorder for 7 days (intervention branch). Patients admitted for cryptogenic stroke will be included. Enrollment and randomization of patients will be carried out during the index case hospitalization, while follow-up will be done on an outpatient basis until day 7.
Validation of ECG Measurement and Atrial Fibrillation Detection
Atrial FibrillationAtrial fibrillation (AFib) is a common type of cardiac arrhythmia in clinical practice, affecting millions of people worldwide. Early detection and treatment of atrial fibrillation are crucial in preventing serious complications such as stroke and heart failure. In recent years, with the flourishing development of wearable devices and mobile technology, electrocardiogram (ECG) measurement applications embedded in smartwatches have gradually become a non-invasive and convenient method for heart rate monitoring. However, the accuracy of these devices has not yet been fully determined. This study aims to verify the ECG measurement and atrial fibrillation detection function of the ASUS ECG App. The accuracy of the ECG application in detecting atrial fibrillation and measuring ECG will be evaluated by comparison with standard 12-lead ECGs.
Laboratory Assessment of the Concentration of Direct Oral Anticoagulants in Patients With Atrial...
Oral AnticoagulantAtrial Fibrillation1 moreThe incidence of thromboembolic and bleeding event associated with catheter ablation for atrial fibrillation(CAAF) varies from 0.9% to 5% during peri-operative period. Direct oral anticoagulants (DOAC) (such as Rivaroxaban, Dabigatran and Edoxaban) are gradually applied in clinical practice to prevent thrombosis events in patients with AF, but studies have shown that DOAC are also affected by surgery, an invasive procedure, sub-therapeutic, food, renal function and age. However, the pharmacokinetic and pharmacodynamic of DOAC during the peri-operative period of CAAF were lacking in China. The purpose of this study was to evaluate the pharmacokinetics and pharmacokinetics of DOAC in patients with peri-operative atrial fibrillation.
KONVERT-AF - Relevance of Point in Time for Conversion of Acute Atrial Fibrillation
Atrial FibrillationTo investigate if in acute symptomatic atrial fibrillation (AF) the early (>2 hrs but within 12 hrs of the beginning of the arrhythmia) electrical cardioversion leads to a longer recurrence-free interval than the delayed cardioversion (> 36 hrs but < 48 hrs after the beginning of the arrhythmia) within the first 3 months after cardioversion.
Verily Watch Cardio (AF and ECG) Study
Atrial FibrillationThis study is designed to evaluate the performance of the Verily Watch Cardio for recording electrocardiogram (ECG) and photoplethysmography (PPG) signals and detecting suspected atrial fibrillation (AF) episodes, in a free-living environment, in participants at risk for having an AF event.
cliNIcal sCEnarios and Pathophysiology of Atrial Fibrillation
Atrial FibrillationAtrial fibrillation (AF) remains the most common sustained cardiac arrhythmia with prevalence and incidence continuously increasing worldwide. Current guidelines propose an etiological, symptom-based classification of the arrhythmia and mainly focused on its duration with consequent rhythm or rate-control strategies. Moreover, risk scores for atherothrombotic systemic or hemorrhagic events related to atrial fibrillation are principally based on patients cardiovascular history and risk factors. This approach do not consider relevant pathophysiological aspects that may play a pivotal role in triggering or perpetuating the arrhythmia, especially at its first occurrence. At this point, a crucial step would be deeply investigating AF clinical and pathophysiological features to guide a tailored diagnostical and therapeutic approach. Indeed, early recognition and proper characterization of triggers, substrates, autonomic system imbalance and modulating factors (drugs, electrolytes, etc) are of the utmost importance for AF care and management. Therefore, this large scale prospective observational study aims to evaluate clinical and pathophysiological features of patients with symptomatic and asymptomatic atrial fibrillation in different scenarios to understand possible distinctive characteristics warranting a personalized approach to the arrhythmia.
Atrial Fibrillation Monitoring on Patients With Lymphoma After Chemotherapy
Atrial FibrillationThis prospective cohort study is to investigate the incidence of atrial fibrillation after chemotherapy by aplying wearable ECG recoder and the risk factors on patients with newly diagonsed lymphoma
Evaluation of 3D Simulation and Planning of Left Atrial Appendage Occlusion Based on 3D Echocardiography...
ATRIAL APPENDAGE CLOSURE for ATRIAL FIBRILLATIONThis study aims to compare a semi-automatic 3D echocardiography-based left atrial appendix occlusion procedure planning with FEops, with other imaging modalities for evaluating the left atrial appendage dimensions and device prediction (sizing, deformation) pre-left atrial appendix occlusion , including the current "gold standard", CT- based FEops HEARTguideTM left atrial appendix occlusion procedure planning. A number of pre-specified endpoints are defined for analyzing this new approach.
Prediction of 30-Day Readmission Using Machine Learning
InfectionHeart Failure7 moreThis is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict the likelihood of 30-day readmission throughout a patient's admission. This algorithm was then validated in a validation cohort.